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What data engineering means for LLM applications
A useful way to understand an LLM application is as two connected workflows: a data lifecycle that prepares knowledge for use, and a request-time workflow that retrieves relevant knowledge and gives it to a model. The first workflow starts with source systems and continues through validation, transformation, indexing, refresh, and governance. The second uses a user’s request to find context, combines that context with the request, and sends the augmented prompt to the LLM.
That makes data engineering a continuing part of the application, not a one-time upload before launch. Source changes, deletions, access rules, and shifts in how content is formatted can all affect what the system can retrieve and what it says. AWS Prescriptive Guidance’s Data lifecycle in generative AI describes the preparation stages; Databricks’ RAG documentation, last updated 15 September 2026, covers retrieval, evaluation, monitoring, and governance in the application lifecycle.
RAG is retrieval, not model training
Retrieval-augmented generation connects an LLM to an external source of information. When a question arrives, the application searches its configured sources for relevant material and includes selected results in the model’s prompt. The model then generates a response using that context along with its learned capabilities.
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This differs from training or fine-tuning a model, which changes model parameters using training data. RAG instead supplies context at answer time, so a team can make newer or more specific information available without putting every update into model training. It is not a correctness guarantee: poor source material, missing context, an ineffective search, or a model’s misinterpretation can still lead to a wrong answer. The system should make it possible to trace answers to the information that was retrieved and to test whether that information supports the response.
How to build a data pipeline for RAG
Build from the user’s task and the authoritative sources needed to answer it. Then treat preparation and retrieval as distinct stages that can be tested and maintained independently.
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- Define the task and choose sources. Identify the questions the application must handle and the systems that contain authoritative answers. Sources may include structured records, such as database or warehouse data, and unstructured material, such as policies, manuals, web pages, or transcripts. Record ownership and the metadata needed to identify, filter, and govern each source.
- Ingest changes, not just initial content. Capture new and updated material, and define how deletions are reflected in the knowledge base. A pipeline that only adds documents can continue retrieving information that has been withdrawn or superseded. AWS’s 2024 article Data governance in the age of generative AI discusses cataloging and refresh and deletion as governance concerns.
- Filter, parse, and validate. Exclude irrelevant or redundant material; parse formats such as HTML, JSON, and plain text; and check that the resulting content and metadata meet the application’s requirements. Where source systems lack useful fields, enrich records with metadata such as source identity, owner, update date, or confidentiality classification.
- Apply privacy controls before indexing. Depending on policy and use case, sensitive information may need redaction, masking, tokenization, or stricter access restrictions. Check that a transformation does not remove context the application needs to interpret a passage. Privacy protection and answer quality need to be assessed together.
- Choose chunking, embeddings, and search. Divide content into retrieval units, select an embedding approach suited to the domain, and choose indexing and search methods. AWS describes fixed token-sized chunks, hierarchical units such as sections and chapters, and semantic units that preserve a coherent idea. These are alternatives to evaluate, not a universal ranking.
- Test retrieval and end-to-end answers. Use representative questions and source material to check whether the system retrieves useful context and produces answers that are supported by it. Evaluate the preparation and retrieval components as well as the full application.
- Operate and refresh the system. Monitor application quality, latency, and cost; detect source or application drift; and update the pipeline as the underlying data changes. Maintain a way to investigate which source material was available to the application and what it retrieved for a response.
Make data AI-ready before choosing an index
Embeddings and a vector index are only part of readiness. Data must also be fit for the task, understandable, governed, and appropriately accessible. The UK public-sector framework Making government datasets ready for AI, published 19 January 2026 by the Government Digital Service, the Department for Science, Innovation and Technology, and the Department for Digital, Culture, Media and Sport, defines AI-ready data as “accurate, complete, consistent, secure, and enriched with metadata so it can be trusted and understood by both humans and machines.” That is UK government guidance; the qualities are a useful general checklist, not a claim that one framework governs every organization.
- Accuracy and completeness: Verify that source records are authoritative and contain the fields or context required for the intended questions.
- Consistency: Resolve conflicting formats, naming conventions, and representations where they could make relevant information hard to find or interpret.
- Metadata and cataloging: Preserve information about where content came from, who owns it, when it was created or updated, and how it may be used.
- Privacy and security: Classify sensitive material and ensure its handling and exposure match organizational policy.
- Stewardship and refresh: Assign responsibility for keeping source data and derived indexes current, including reflecting corrections and removals.
Choose retrieval methods to match the data
RAG does not mean sending every source through a vector database. Databricks identifies vector stores, keyword search, and SQL databases as possible retrieval sources. The right choice depends on the question and on how the source represents its information. A system may combine a warehouse or database for structured facts with document retrieval for policies or manuals.
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| Source or retrieval approach | Useful fit | Design question |
|---|---|---|
| Structured tables or SQL databases | Facts represented as fields and records, such as values that need to be filtered or queried. | Can the question be answered from structured fields, and are the records current and authorized for this user? |
| Document stores with keyword search | Documents or passages where exact terms and phrases help locate relevant material. | Do the source text and its metadata support reliable searching and filtering? |
| Vector stores with embedding-based retrieval | Content where finding semantically related passages is useful even when wording differs. | Do the chunks and embedding approach retrieve relevant context for representative domain questions? |
| Combined retrieval sources | Tasks needing both structured facts and unstructured context. | How will results from different sources be authorized, reconciled, and evaluated together? |
These are design patterns, not a vendor ranking. The reviewed documentation does not establish a neutral benchmark that makes one database or retrieval method best for every workload.
Keep permissions, provenance, and security in the retrieval path
A knowledge base can contain information that different users or tenants are not all allowed to see. The application must enforce access controls when retrieving context, not rely only on the model to avoid disclosing it. Preserve source identity and relevant metadata through ingestion and indexing so results can be checked and traced.
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- Apply user, role, tenant, and document-level rules when selecting retrieval results; test that one user’s permitted context cannot leak into another user’s answers.
- Protect source data, indexes, embeddings, prompts, and logs with appropriate access controls and encryption in storage and transit.
- Validate content before ingestion to reduce the risk that malicious documents manipulate application behavior, and validate outputs for privacy or policy violations.
- Retain enough provenance to identify the source and version of retrieved material when investigating an answer or an access decision.
AWS Prescriptive Guidance on secure access for generative AI and AWS’s governance article describe risks including unauthorized access, data exfiltration, malicious content, and provenance failures. These risks arise across the lifecycle, so security cannot be treated as an index setting alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the pipeline as well as the answer
During development, test against the application’s business requirements. A useful evaluation examines whether the preparation pipeline preserves needed content, whether retrieval finds relevant and sufficient evidence, and whether the final answer is supported by that evidence. End-to-end answers alone can hide a failure in parsing, chunking, filtering, or retrieval.
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In production, monitor the deployed system rather than assuming development results will hold indefinitely. Track quality alongside latency and cost, and investigate changes in source data, formatting, or application behavior. For example, a formatting change may alter how content is parsed and chunked, which can change what later searches retrieve. Databricks’ RAG guidance distinguishes development-time evaluation from production monitoring and identifies application quality, cost, and latency as operational concerns.
Compare designs against the workload
Before committing to a data and retrieval design, compare options against the same representative questions and source material. Consider:
- Whether sources are structured, unstructured, or a combination.
- How frequently sources change and how updates and deletions reach the index.
- Retrieval quality for the actual questions users ask.
- Whether permissions, tenant isolation, and provenance can be enforced and audited.
- How well the design supports component-level evaluation and production monitoring.
- Operational complexity, cost, and latency for the required service.
No single chunk size, embedding model, index, or vendor is established as universally best. The design should follow the application’s source material, access rules, freshness needs, and measured behavior.
Further reading
For a broader technical treatment, Chip Huyen’s AI Engineering (O’Reilly, December 2024; ISBN 9781098166298) covers dataset engineering, data curation and quality, RAG, evaluation, and production architecture. It is a related book on AI engineering rather than a dedicated textbook covering all of data engineering.
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